波兰文本历时规范化的两种方法
Two Approaches to Diachronic Normalization of Polish Texts
AI总结：
本文提出并比较了波兰文本历时规范化的规则驱动与基于T5的神经两种方法，实验表明规则方法在4个数据集变体中的3个上表现更优，但两者各有优劣。
摘要：
本文讨论了波兰文本历时规范化的两种方法：一种基于一组手工模式（handcrafted patterns）的规则驱动方案，以及一种基于文本到文本迁移变换器（text-to-text transfer transformer，简称T5）架构的神经规范化模型。文中详细介绍了为该任务准备的训练与评估数据，以及为比较所提出的规范化方案而进行的实验。进行了定量与定性分析。结果表明，在当前对该问题的研究阶段，规则驱动方案在所准备数据集的4个变体中的3个上优于神经方案，尽管在实践中两种方法各有明显的优缺点。
英文摘要：
This paper discusses two approaches to the diachronic normalization of Polish texts: a rule-based solution that relies on a set of handcrafted patterns, and a neural normalization model based on the text-to-text transfer transformer architecture. The training and evaluation data prepared for the task are discussed in detail, along with experiments conducted to compare the proposed normalization solutions. A quantitative and qualitative analysis is made. It is shown that at the current stage of inquiry into the problem, the rule-based solution outperforms the neural one on 3 out of 4 variants of the prepared dataset, although in practice both approaches have distinct advantages and disadvantages.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
